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    MARK R. JURY Oceanogmphy Department, University of Cape Town, Rondebosch, South Africa

    Received 22 May 1994 Accepted I5 May 1995


    Climatic determinants of southern African summer rainfall are analysed using statistical techniques. Summer rainfall time series are formulated for South Africa, Namibia and Zimbabwe areas and correlated with global indices and with field variables: sea- surface temperature (SST), outgoing longwave radiation (OLR), and tropospheric winds at 200 and 700 hPa levels. Linear regression correlations are performed using monthly standardized departures at various lags before and during the summer season.

    The SSTs in the central equatorial Indian Ocean (CEI) are identified as significant predictors/modulators of southern African rainfall. The SSTs at the CEI are best correlated with South Africa rainfall at r < -0.6 at lags - 2 and 0 months and are associated with the El Nifio. The SSTs of the CEI modulate the overlying monsoon trough, as indicated by the OLR correlation maps. A centre of convective action alternates between southern Africa and the south-west Indian Ocean from year-to-year.

    A useful circulation index that emerges in the statistical analysis is spring-time zonal upper wind anomalies over the equatorial central Atlantic. This index is correlated with South Africa rainfall at r < -0.8 at lags - 4 and - 2 months. Westerly (easterly) 200 hPa anomalies in spring are followed by a summer of below (above) normal rainfall. Other patterns that have a bearing on summer rainfall include a circulation gyre identified in 700 hPa wind correlations off the coast of south-east Africa. This circulation feature controls the flux of moisture between southern Africa and the northern Mozambique Channel.

    The correlation patterns offer statistical guidance in long-range forecasts and insights to climatic processes that govern the interannual variability of summer rainfall over southern Africa.

    KEY WORDS: inter-annual climatic fluctuations; statistical teleconnections; Southern Afnica; summer rainfall; predictability; linear regression; rainfall index; circulation index; correlation patterns


    Summer (November to March) rains over the plateau of southern Africa (18-3OoS, 18-32"E, Figure 1) average about 500 mm and rarely offset potential evaporation (Jury et al., 1993a; Levey, 1993). The imbalance means that climatic variations from year-to-year have critical impacts on crop yields and the sustainability of water resources. Since the 197Os, fluctuations in rainfall over southern Africa have become more extreme (Jury et al., 1993b). The last period of South African summer rainfall within 0.5 of the standard deviation for three consecutive years was 1958-1960 (Figure 2(a)). Area-averaged rainfall exceeded + 0.9 times the standard deviation in 1974, 1976, and 1988 and declined to below - 0.7 times the standard deviation in 1983 and 1992. Maize production in central southern Africa (25"-26"S, 25"-26"E) has fluctuated from 292 M ton per planted km2 in the 1989 La Niiia to 19 M ton per planted km2 during the devastating 1992 El Niiio, which is an order of magnitude decline in crop yield.

    Repeated drought in recent years has given impetus to statistical studies of the precursor role of sea-surface temperature (SST) anomalies, particularly in the tropical band, in upsetting the balance of continental and marine rainfall. Coherent spectral peaks in the 2-5 year range in both southern African rainfall and SST over the surrounding subtropical oceans have been uncovered (Nicholson, 1989). Tyson (1986), Nicholson and Entekhabi

    CCC 0899-841 8/96/020135-19 0 1996 by the Royal Meteorological Society

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    Figure 1. Location map of southem Africa showing index locations used in the statistical analysis.

    (1987) and Lindesay (1 988) have linked these to regional circulation adjustments that involve the El Niiio- Southern Oscillation (ENSO) phenomenon.

    Walker (1 989) utilized monthly gridded ship observations of SST, surface wind, and weather parameters over the south-east Atlantic and south-west Indian Oceans in statistical associations with South African plateau rainfall. In non-El Niiio years reliable correlations with the adjacent Agulhas and Benguela SST were noted, in agreement with Nicholson and Entekhabi (1 987). Rainfall increased when SSTs in the subtropical band of the eastern Agulhas Current were above normal (r > + 0.4) both during and prior to the season. Surface wind composites indicated increased easterly flow in wet summers in the subtropical band, particularly to the east of Madagascar. Walker (1989) identified the Agulhas Current retroflection region to the south of Cape Town as an important region. Differences between composite wet and dry summers highlighted that SST and heat fluxes in the retroflection were 10 per cent above the mean in wet years.

    Mason (1 992) considered SST in the south-west Indian Ocean and South Atlantic using principal component analysis. Areas explaining changes in SST in the region included the south-east tropical Atlantic (17 per cent), the southern Mozambique Channel (13 per cent), the western tropical Atlantic (1 1 per cent), and the western Indian Ocean (7 per cent). Key areas of variability in the west Indian Ocean and Agulhas region were found through correlation analysis in respect of summer rainfall. A coherence of rainfall and SST cycles, particularly for the 18 year period in the South Atlantic, was identified.

    Mason (1 992) considered the phase of the stratospheric quasi-biennial oscillation (QBO), equatorial zonal wind anomaly on southern African rainfall. Stratospheric QBO-troposphere interactions have interesting associations with the El Niiio-Southern Oscillation which make it an integral part of long-range forecast scenarios. Mason (1992) found that the correlation between the SO1 and summer rainfall is above + 0.6 in a wide axis between 23" and 29"E when the QBO is in west phase. The relationship may be unstable as in the 1991/1992 summer, when drought corresponded with an east phase QBO. Relationships between western Indian Ocean SST and the QBO were also identified.

    Jury et al. (1994) found a correlation pattern between the QBO and tropospheric winds, which corresponds with upper anticyclonic, Walker Cell uplift over southern Africa and descent over Madagascar in west phase summers. This circulation mechanism is consistent with opposing outgoing long wave radiation (OLR) correlation values between southern Afrrca and the south-west Indian Ocean. Given the quasi-regular period of the QBO (2.3 years), this global indicator offers additional levels of predictability for southern African rainfall.

    A number of researchers have looked beyond statistical associations between SST and rainfall; and have analysed 'intervening' variables to reveal some of the dynamical mechanisms underlying dry and wet summers. A number of atmospheric 'signals' which anticipate southern African summer rainfall are found over the surrounding oceans.



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    Figure 2. Summer season areal rainfall index time series for the three areas. Bold line is smoothed with 1-2-1 filter.

    Rocha (1992) studied regional teleconnections driving rainfall variability over south-east Africa. He utilized the Comprehensive Ocean Atmosphere Data Set (COADS), including the central Indian Ocean in the analysis. The correlation results and subsequent general circulation model (GCM) experiments demonstrated the critical impact of central Indian Ocean SST on south-east Africa summer rainfall. The PC1 SST variance was identified as the ENS0 signal in the central equatorial Indian (CEI) and eastern equatorial Pacific oceans. In dry years the CEI (0- lo's, 60-80"E) warms in sympathy with the eastern Pacific. Similar field correlations were performed with surface pressure. Negative values over an axis from Zimbabwe to Marion Island contrasted with positive values near Mauritius and near Gough Island on the South Atlantic in the preceding spring. These patterns shifted northward during summer such that a H-L-H pattern emerged in the 0-20"s band over the Atlantic-Africa-Lndian areas, respectively, in wet phases. Similar correlations were analysed with winds, temperatures, and humidity at various tropospheric levels. Rocha (1 992) identified the direction of moisture transport anomalies in the Indian Ocean as an important climatic determinant of south-east Africa rainfall. Vertical heat flux anomalies were investigated but only incoherent patterns were noted.

    D'Abreton's research (1 992) suggested that daily-seasonal convective variability was governed by responses to SST anomalies in the west Indian Ocean. He also confirmed the findings of Rocha (1992) on water vapour flux

  • 138 M. R. JURY

    transport anomalies in wet and dry years. D' Abreton (1992) also advanced our knowledge of energy conversions in tropical-temperate troughs contributed via ocean-atmosphere forcing.

    Pathack (1993) used the COADS 2" SST data set and established the central equatorial Indian Ocean region as a key determinant of late summer rainfall variability over the plateau of South Africa. He analysed OLR, which is well correlated with interannual departures of summer rainfall, and tropospheric winds, and revealed the structure and extent of thermodynamic modulation of the atmosphere by the ocean. A major contribution of Pathack (1 993) was in distinguishing the varying seasonal contributions from the Atlantic and Indian Oceans. He found that wanning of the central South Atlantic enhances November rains over southern Africa, whereas cooling in the CEI stimulates February rains. February rainfall versus previous October OLR correlations were + 0.9 over the CEI at O"S, 70"E.

    Numerical model simulations of the global ENS0 signal and its regional impacts have afforded an improved understanding of the dynamics of ocean-atmosphere interaction with regard to wet and dry summers over southern Africa. Prescribed changes in the SST field and analysis of the circulation and rainfall response patterns were conducted by Jury and Pathack (1993) and Mason et al. (1994). In the former study, SSTs in the CEI were increased by 2C for an extended period consistent with an ENSO wann phase. Following a 3-year model run with the CSIRO4 GCM, late summer rainfall was halved. These numerical analyses provide insight to seasonal processes underlying fluctuations in regional rainfall.

    An accurate forecast of southern African summer rainfall 2 4 months in advance would be of considerable strategic economic value. Understanding the kinematic and thermodynamic mechanisms and regional teleconnection patterns precursor to, and associated with, wet and dry years could benefit environmental and agricultural management practices.

    To assess climatic determinants governing interannual fluctuations of convection over southern Africa and the adjacent oceans, a statistical study was conducted. Spatial correlation patterns for various meteorological variables were analysed for the austral spring and summer period. In this paper the regional teleconnection patterns associated with seasonal rainfall variability over three distinct areas of southern Africa are investigated.


    Area-averaged summer rainfall indices are constructed from station data for three areas, namely north-central South Africa (24"-28"S, 24"-29"E), north-east Namibia (19-20"S, 18-19"E) and Zimbabwe (1 8"-2O"S, 28"- 3 1 "E) as shown in Figure 1. These are computed from the time series of rainfall standardized departures for the months November to March, for each station within a prescribed, climatically homogeneous area. The rainfall index represents the overall seasonal departure of relevance to agricultural productivity. The index time series are shown in Figure 2(a-c).

    The reference rainfall index time series for the three areas were cross-correlated with each other and with global indices, such as the Southern Oscillation Index (SOI) and the Quasi-Biennial Oscillation (QBO), of stratospheric zonal winds. The amplitude of temporal cycles was determined using a periodogram analysis of fast Fourier transforms in the Statgraphics software package. To establish spatial correlation patterns, gridded fields of sea-surface temperature, outgoing longwave radiation, and tropospheric winds at 200 hPa and 700 hPa levels were utilized. The field data derive fiom the Climate Analysis Center (CAC), Washington, DC and from the Comprehensive Ocean Atmosphere Data Set (Slutz ef al., 1985) at 5" and 4" resolution, respectively. The sources of data, interpolation schemes, and data quality provide adequate confidence in statistical analysis (Pathack, 1993), although conventional observations are relatively sparse in the tropical and oceanic regions around southern Africa. Global indices were computed fiom CAC data in the usual form: SO1 is the Tahiti-Darwin pressure anomaly, and the QBO is the Singapore 30 hPa zonal wind departure. Here the SO1 and QBO are averaged for the months January and February, when southern Africa rainfall reaches the seasonal peak.

    All index and gridded data were converted to standardized departures using the historical mean and standard deviation at each grid-point, in preparation for correlation analysis. Point-to-field maps were computed using the Pearson's product-moment linear regression technique (Jury et al., 1992; Pathack, 1993) at lags - 4, - 2, 0, and


    + 2 months, which refer to September, November, January, and March. For brevity, lags - 4 and + 2 are omitted. In addition, for selected index areas, namely the central equatorial Indian Ocean in respect of SST and the equatorial central Atlantic (ECAU) in respect of the upper zonal wind, the temporal lag correlation statistics are analysed at various lags from - 5 to - 1 month.

    The data are of varying lengths, so statistical significance is reached at different r values. The rainfall, global indices, and SSTextend from 1955 to 1988, hence the number of degrees of freedom (DF) is 34 and [r] > 0.28 are significant at the 95 per cent confidence limit. The CAC wind data at 200 hpa and 700 hPa extend from 1968 to 1987 and statistical significance is reached with [r] > 0.38. The OLR data series is rather short, with only 11 DF, and [r] > 0.48 are statistically significant. Meaning is attached to the correlation pattern if four adjacent grid-points have [r] values in excess of the above-mentioned criteria. Some degree of subjectivity is inherent...


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